What are the differences between Sigmoid and ReLU activation functions?

What are the differences between Sigmoid and ReLU activation functions?

Sigmoid is a smooth, S-shaped curve that outputs values between 0 and 1, making it suitable for binary classification. ReLU, or Rectified Linear Unit, outputs the input directly if it's positive; otherwise, it outputs zero, which helps with faster training.

When should I use Sigmoid over ReLU?

You should use Sigmoid when you need probabilities for binary outcomes, especially in the output layer of binary classification models. ReLU is generally preferred in hidden layers due to its ability to mitigate the vanishing gradient problem.

What are the advantages of using ReLU?

ReLU is computationally efficient, helps with faster convergence during training, and reduces the likelihood of vanishing gradients, making it a popular choice in deep learning.

Are there any drawbacks to using Sigmoid?

Yes, Sigmoid can cause vanishing gradients, especially in deep networks, leading to slow convergence. It also outputs values that are not zero-centered, which can affect optimization.